Introduction to Probability Simulation and Gibbs Sampling With R (Paperback)

Introductory Examples: Simulation, Estimation, and Graphics.- Generating Random Numbers.- Monte Carlo Integration and Limit Theorems.- Sampling from Applied Probability Models.- Screening Tests.- Markov Chains with Two States.- Examples of Markov Chains with Larger State Spaces.- to Bayesian Estimation.- Using Gibbs Samplers to Compute Bayesian Posterior Distributions.- Using WinBUGS for Bayesian Estimation.- Appendix: Getting Started with R.